2025 07 16

GCP – Implementing High-Performance LLM Serving on GKE: An Inference Gateway Walkthrough

The excitement around open Large Language Models like Gemma, Llama, Mistral, and Qwen is evident, but developers quickly hit a wall. How do you deploy them effectively at scale?

Traditional load balancing algorithms fall short, as they fail to account for GPU/TPU load status, leading to inefficient routing for computationally intensive AI inference with its highly variable processing times. This directly impacts serving performance and the user experience.

This guide demonstrates how Google Kubernetes Engine and the new GKE Inference Gateway together offer a robust and optimized solution for high-performance LLM serving, specifically by overcoming the limitations of traditional load balancing with smart routing aware of AI-specific metrics like pending prompt requests and critical KV Cache utilization.

We’ll walk through deploying an LLM using the popular vLLM framework as the inference backend. We’ll use Google’s gemma-3-1b-it model and NVIDIA L4 GPUs as a concrete, easy-to-start example (avoiding the need for special GPU quota requests initially). The principles and configurations shown here apply directly to larger, more powerful models and diverse hardware setups.

Why Use GKE Inference Gateway for LLM Serving?

GKE Inference Gateway isn’t just another ingress controller; it’s purpose-built for the unique demands of generative AI workloads on GKE. It extends the standard Kubernetes Gateway API with critical features:

Intelligent load balancing: Goes beyond simple round-robin. Inference Gateway understands backend capacity, including GPU-specific metrics like KV-Cache utilization, to route requests optimally. For LLMs, the KV-Cache stores the intermediate attention calculations (keys and values) for previously processed tokens. This cache is the primary consumer of GPU memory during generation and is the most common bottleneck. By routing requests based on real-time cache availability, the gateway avoids sending new work to a replica that is near its memory capacity, thus preventing performance degradation and maximizing GPU usage, increasing throughput, and reducing latency.
AI-aware resource management: Inference Gateway recognizes AI model serving patterns. This enables advanced use cases like serving multiple different models or fine-tuned variants behind a single endpoint. It is particularly effective at managing and multiplexing numerous LoRA adapters on a shared pool of base models. This architecture dramatically increases model density on shared accelerators, reducing costs and operational complexity when serving many customized models. It also enables sophisticated, model-aware autoscaling strategies (beyond basic CPU/memory).
Simplified operations: Provides a dedicated control plane optimized for inference. It seamlessly integrates with GKE, offers specific inference dashboards in Cloud Monitoring, and supports optional security layers like Google Cloud Armor and Model Armor, reducing operational overhead.
Broad model compatibility: The techniques shown work with a wide array of Hugging Face compatible models.
Flexible hardware choices: GKE offers access to various NVIDIA GPU types (L4, A100, H100, etc.), allowing you to match hardware resources to your specific model size and performance needs. (See GPU platforms documentation).

The Walkthrough: Setting Up Your Inference Pipeline

Let’s get started building out our inference pipeline. By following these steps, you will deploy and configure the essential infrastructure to serve your LLMs with the high performance and scalability demanded by real-world applications, built on GKE and optimized by the Inference Gateway.

Environment Setup

Ensure your Google Cloud environment is ready. All steps in this walkthrough are tested in Google Cloud Shell. Cloud Shell has the Google Cloud CLI, kubectl, and Helm pre-installed.

1. Google Cloud project: Have a project with billing enabled.

code_block: <ListValue: [StructValue([(‘code’, ‘export PROJECT_ID=”your-project-id”rngcloud config set project $PROJECT_ID’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cddc10>)])]>

2. Google Cloud CLI: Ensure gcloud is installed and updated. Run gcloud init if needed.

3. kubectl: Install the Kubernetes CLI: gcloud components install kubectl

4. Helm: Install the Helm package manager (Helm installation guide).

5. Enable APIs: Activate necessary Google Cloud services.

code_block: <ListValue: [StructValue([(‘code’, ‘gcloud services enable \rn container.googleapis.com \rn compute.googleapis.com \rn networkservices.googleapis.com \rn monitoring.googleapis.com \rn logging.googleapis.com \rn modelarmor.googleapis.com \rn –project=$PROJECT_ID’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cddf10>)])]>

6. Configure permissions (IAM): Grant required roles. Remember to follow the principle of least privilege in production environments.

code_block: <ListValue: [StructValue([(‘code’, ‘export USER_EMAIL=$(gcloud config get-value account) # Or your service account emailrnrn# Grant necessary roles (adjust for production)rngcloud projects add-iam-policy-binding $PROJECT_ID –member=”user:${USER_EMAIL}” –role=”roles/container.admin” –condition=Nonerngcloud projects add-iam-policy-binding $PROJECT_ID –member=”user:${USER_EMAIL}” –role=”roles/compute.networkAdmin” –condition=Nonern# You might need roles/iam.serviceAccountUser depending on your node service account setup’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cddee0>)])]>

7. Set region: Choose a region with the GPUs you need.

code_block: <ListValue: [StructValue([(‘code’, ‘export REGION=”us-central1″ # Example regionrngcloud config set compute/region $REGION’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cdd190>)])]>

8. Hugging Face token: Obtain a Hugging Face access token (read permission minimum). If using Gemma models, accept the license terms on the Hugging Face model page.

code_block: <ListValue: [StructValue([(‘code’, ‘export HF_TOKEN=”your-huggingface-token”‘), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cdd400>)])]>

Create GKE Cluster Resources

Set up the GKE cluster and necessary networking components.

1. Proxy-only subnet (run once per region/VPC): Required for Inference Gateway’s regional load balancer.

code_block: <ListValue: [StructValue([(‘code’, ‘export VPC_NETWORK_NAME=”default” # Or your specific VPC networkrnexport PROXY_SUBNET_RANGE=”10.120.0.0/23″ # Choose an unused rangernexport PROXY_SUBNET_NAME=”proxy-only-subnet-${REGION}”rnrngcloud compute networks subnets create $PROXY_SUBNET_NAME \rn –purpose=REGIONAL_MANAGED_PROXY \rn –role=ACTIVE \rn –region=$REGION \rn –network=$VPC_NETWORK_NAME \rn –range=$PROXY_SUBNET_RANGE \rn –project=$PROJECT_ID || echo “Proxy subnet ‘${PROXY_SUBNET_NAME}’ may already exist.”‘), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cddfa0>)])]>

2. GKE standard cluster: Inference Gateway currently requires a Standard cluster.

code_block: <ListValue: [StructValue([(‘code’, ‘export CLUSTER_NAME=”llm-inference-cluster” # Choose a namernrngcloud container clusters create $CLUSTER_NAME \rn –project=$PROJECT_ID \rn –region=$REGION \rn –release-channel=stable \rn –machine-type=e2-standard-4 `# Basic type for default pool` \rn –num-nodes=1 \rn –network=$VPC_NETWORK_NAME \rn –enable-ip-alias `# Required for VPC-native` \rn –gateway-api=standard `# Enable Gateway API support` \rn –scopes=https://www.googleapis.com/auth/cloud-platformrn # –subnetwork=YOUR_PRIMARY_SUBNET_NAME # Optional: Specify if not using ‘default’ subnetrnrn# Cluster creation takes several minutes…’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cdd9d0>)])]>

3. Configure kubectl:

code_block: <ListValue: [StructValue([(‘code’, ‘gcloud container clusters get-credentials $CLUSTER_NAME –region $REGION –project $PROJECT_ID’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cdd6a0>)])]>

4. Accelerator node pool: Add nodes with GPUs. Ensure you have quota for the chosen GPU type and the zone supports it.

code_block: <ListValue: [StructValue([(‘code’, ‘# Verify zone availability for your chosen GPUrnexport ACCELERATOR_ZONES=”us-central1-a” # Example zone for L4 in us-central1rnexport ACCELERATOR_POOL_NAME=”l4-pool”rnexport GPU_TYPE=”nvidia-l4″rnexport MACHINE_TYPE=”g2-standard-8″ # G2 machine type compatible with L4rnrngcloud container node-pools create $ACCELERATOR_POOL_NAME \rn –cluster=$CLUSTER_NAME \rn –project=$PROJECT_ID \rn –region=$REGION \rn –node-locations=$ACCELERATOR_ZONES \rn –machine-type=$MACHINE_TYPE \rn –accelerator type=$GPU_TYPE,count=1,gpu-driver-version=latest `# Request 1 L4 GPU per node` \rn –enable-autoscaling \rn –num-nodes=1 `# Initial node count` \rn –min-nodes=1 \rn –max-nodes=3 `# Adjust max scaling as needed` \rn –disk-size=100Gi `# Adjust based on model size` \rn –enable-gvnic `# Recommended for improved networking` \rn –scopes=https://www.googleapis.com/auth/cloud-platformrnrn# Node pool creation also takes several minutes…’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cdd7c0>)])]>

Install Gateway API and Inference Gateway CRDs

Apply the Custom Resource Definitions (CRDs) that define the necessary Kubernetes objects.

NOTE: Using kubectl apply with remote URLs means you’re fetching the manifests at execution time. For production, consider vendoring these manifests or referencing specific tagged releases.

code_block: <ListValue: [StructValue([(‘code’, “# — Standard Gateway API CRDs —rn# Provides the base Gateway, HTTPRoute, etc. resourcesrnkubectl apply -f https://github.com/kubernetes-sigs/gateway-api/releases/download/v1.0.0/standard-install.yamlrnrn# — Required GKE Gateway CRDs —rn# Needed for GKE’s Gateway controller implementation (BackendPolicy, HealthCheckPolicy)rnkubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/gke-gateway-api/main/config/crd/networking.gke.io_gcpbackendpolicies.yamlrnkubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/gke-gateway-api/main/config/crd/networking.gke.io_healthcheckpolicies.yamlrnrn# — Optional but Recommended GKE Gateway CRDs —rn# Enable advanced GKE-specific features (SLA, Security, Affinity, etc.)rnkubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/gke-gateway-api/main/config/crd/networking.gke.io_gcpgatewaypolicies.yamlrnkubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/gke-gateway-api/main/config/crd/networking.gke.io_gcproutingextensions.yamlrnkubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/gke-gateway-api/main/config/crd/networking.gke.io_gcpsessionaffinitypolicies.yamlrnkubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/gke-gateway-api/main/config/crd/networking.gke.io_gcptrafficextensions.yamlrnrn# — Inference Gateway CRDs —rn# Defines InferencePool and InferenceModel resourcesrn# Check releases for the latest stable version (e.g., v0.3.0)rn# –> IMPORTANT: Always check the official docs for the recommended version <–rnkubectl apply -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/releases/download/v0.3.0/manifests.yamlrnrn# — Create Hugging Face Token Secret —rn# Securely stores your HF token for the pods to usernkubectl create secret generic hf-secret \rn –from-literal=hf_api_token=$HF_TOKEN \rn –dry-run=client -o yaml | kubectl apply -f -“), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cdd100>)])]>

NOTE: You might see warnings about missing annotations if GKE pre-installed some base CRDs; these are generally safe to ignore during initial setup.

Deploy the LLM Inference Server (using vLLM)

First, create the Kubernetes Secret to securely store your Hugging Face token, which the deployment will need to download the model.

code_block: <ListValue: [StructValue([(‘code’, ‘# — Create Hugging Face Token Secret —rn# Securely stores your HF token for the pods to usernkubectl create secret generic hf-secret \rn –from-literal=hf_api_token=$HF_TOKEN \rn –dry-run=client -o yaml | kubectl apply -f -‘), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef514cddf40>)])]>

Now, define and apply the Kubernetes Deployment for the pods running the vLLM server with our chosen model. Inference Gateway will route traffic to these pods.

Key configurations in the YAML:

metadata.labels.app: Crucial! The InferencePool will use this label to find the pods.
spec.template.spec.containers[0].resources: Must match the GPU node pool (e.g., nvidia.com/gpu: "1" for one L4).
spec.template.spec.containers[0].env.MODEL_ID: Set to the Hugging Face model ID.
spec.template.spec.nodeSelector: Ensures pods land on the GPU nodes.
spec.template.spec.containers[0].*Probe: Vital for health checks and readiness signals to the Gateway.

Save as llm-deployment.yaml:

code_block: <ListValue: [StructValue([(‘code’, ‘apiVersion: apps/v1rnkind: Deploymentrnmetadata:rn name: gemma-3-1b-deployment # Descriptive namernspec:rn replicas: 1 # Start with 1 replica; HPA can scale this laterrn selector:rn matchLabels:rn app: gemma-3-1b-server # ** Label for InferencePool selector **rn template:rn metadata:rn labels:rn app: gemma-3-1b-server # ** Label for InferencePool selector **rn ai.gke.io/model: gemma-3-1b-it # Metadata label (optional but good practice)rn ai.gke.io/inference-server: vllm # Metadata label (optional but good practice)rn spec:rn terminationGracePeriodSeconds: 60 # Allow time for graceful shutdownrn containers:rn – name: vllm-inference-serverrn # –> NOTE: Always check for the latest recommended vLLM image <–rn image: us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20250312_0916_RC01rn resources:rn requests:rn cpu: “2”rn memory: “10Gi”rn ephemeral-storage: “10Gi”rn nvidia.com/gpu: “1” # ** Request 1 GPU **rn limits:rn cpu: “2”rn memory: “10Gi”rn ephemeral-storage: “10Gi”rn nvidia.com/gpu: “1” # ** Limit to 1 GPU **rn command: [“python3”, “-m”, “vllm.entrypoints.openai.api_server”]rn args:rn – –model=$(MODEL_ID)rn – –tensor-parallel-size=1 # Adjust for larger models/multi-GPU nodesrn – –host=0.0.0.0rn – –port=8000 # ** Port targeted by InferencePool **rn env:rn – name: MODEL_IDrn value: google/gemma-3-1b-it # ** Target Model ID **rn – name: HUGGING_FACE_HUB_TOKENrn valueFrom:rn secretKeyRef:rn name: hf-secret # Use the secret created earlierrn key: hf_api_tokenrn ports:rn – containerPort: 8000rn name: http # ** Name used by InferencePool targetPort **rn protocol: TCPrn # — Health & Readiness Probes —rn readinessProbe:rn httpGet:rn path: /health # vLLM OpenAI endpoint health check pathrn port: http # Use the named port ‘http’rn initialDelaySeconds: 60 # Allow time for model download/loadrn periodSeconds: 10rn failureThreshold: 5 # More tolerant during startup/loadrn livenessProbe:rn httpGet:rn path: /healthrn port: httprn initialDelaySeconds: 120rn periodSeconds: 20rn failureThreshold: 3rn startupProbe: # Ensures container is fully ready before marking as ‘live’rn httpGet:rn path: /healthrn port: httprn # Generous timeout for initial model download (~10-15 mins)rn failureThreshold: 90 # 90 failures * 10s period = 15 minutesrn periodSeconds: 10rn initialDelaySeconds: 30rn volumeMounts:rn – name: dshm # Mount /dev/shm for potential inter-process communicationrn mountPath: /dev/shmrn volumes:rn – name: dshmrn emptyDir:rn medium: Memoryrn sizeLimit: “4Gi” # Adjust size as needed, helps performance for some modelsrn nodeSelector:rn # ** Target the correct GPU nodes **rn cloud.google.com/gke-accelerator: nvidia-l4 # Must match the node pool accelerator labelrn # Optional: Specify driver version if neededrn # cloud.google.com/gke-gpu-driver-version: latest’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef515110100>)])]>

Apply the Deployment and wait for the pod(s) to become ready. This includes the time needed to download the model, which can take several minutes depending on model size and network speed.

code_block: <ListValue: [StructValue([(‘code’, ‘kubectl apply -f llm-deployment.yamlrnecho “Waiting for LLM deployment to become available (may take several minutes for model download)…”rn# Increase timeout if deploying very large modelsrnkubectl wait –for=condition=Available –timeout=15m deployment/gemma-3-1b-deployment’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef515110070>)])]>

Configure GKE Inference Gateway Resources

Now, define how the Inference Gateway manages traffic to the deployed model server pods.

1. Create the inference pool: This resource groups the backend pods using the labels defined in the Deployment. We use the official Helm chart for this.

code_block: <ListValue: [StructValue([(‘code’, ‘# Ensure Helm chart version aligns with CRD version installed earlier (e.g., v0.3.0)rnexport CHART_VERSION=”v0.3.0″rnrnhelm install gemma-3-1b-pool \rn oci://registry.k8s.io/gateway-api-inference-extension/charts/inferencepool \rn –version $CHART_VERSION \rn –set inferencePool.modelServers.matchLabels.app=gemma-3-1b-server `# ** MUST MATCH Deployment pod label **` \rn –set inferencePool.modelServers.targetPort.name=http `# ** MUST MATCH Deployment containerPort name **` \rn –set provider.name=gke’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef515110370>)])]>

2. Define the inference model: Specifies metadata about the model served by the pool. Save as gemma-3-1b-inference-model.yaml:

code_block: <ListValue: [StructValue([(‘code’, “apiVersion: inference.networking.x-k8s.io/v1alpha2rnkind: InferenceModelrnmetadata:rn # Name used to reference this specific model resourcern name: gemma-3-1b-it-modelrnspec:rn # This MUST match the MODEL_ID env var in the deploymentrn # AND the ‘model’ field in inference requestsrn modelName: google/gemma-3-1b-itrn criticality: Standard # Or other criticality levels if neededrn poolRef:rn # ** Links this model definition to the InferencePool **rn name: gemma-3-1b-poolrn namespace: default # Assuming default namespace”), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef5151102b0>)])]>

3. Apply: kubectl apply -f gemma-3-1b-inference-model.yaml

4. Define the entry point: The gateway: Creates the actual load balancer. Save as inference-gateway.yaml:

code_block: <ListValue: [StructValue([(‘code’, “apiVersion: gateway.networking.k8s.io/v1rnkind: Gatewayrnmetadata:rn name: inference-gateway # Name for the Gateway resourcernspec:rn # Use GKE’s managed regional external HTTP(S) LBrn gatewayClassName: gke-l7-regional-external-managedrn listeners:rn – protocol: HTTP # ** Use HTTPS (TLS) for production! **rn port: 80 # External port clients connect torn name: httprn allowedRoutes:rn # Only allow routes from the same namespace to attachrn namespaces:rn from: Same”), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef5151105e0>)])]>

5. Apply: kubectl apply -f inference-gateway.yaml Load balancer provisioning takes a few minutes.

6. Route the traffic: The HTTPRoute: Connects requests coming into the Gateway to the correct InferencePool based on path matching. Save as gemma-3-1b-httproute.yaml:

code_block: <ListValue: [StructValue([(‘code’, ‘apiVersion: gateway.networking.k8s.io/v1rnkind: HTTPRouternmetadata:rn name: gemma-3-1b-routernspec:rn parentRefs:rn – name: inference-gateway # ** MUST MATCH Gateway name **rn namespace: default # Assuming default namespacern rules:rn – matches:rn # Route requests starting with /v1 (common for OpenAI-compatible APIs)rn – path:rn type: PathPrefixrn value: /v1rn backendRefs:rn # ** Target the specific InferencePool **rn – name: gemma-3-1b-poolrn group: inference.networking.x-k8s.io # API group for InferencePoolrn kind: InferencePoolrn # Port MUST match the internal port of the backend pods (vLLM default is 8000)rn port: 8000rn weight: 1 # Direct all matching traffic here initially’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef515110760>)])]>

7. Apply: kubectl apply -f gemma-3-1b-httproute.yaml

Verify the Deployment

Let’s check if everything is wired up correctly.

1. Get gateway IP address: Wait for the load balancer to get an external IP.

code_block: <ListValue: [StructValue([(‘code’, ‘echo “Waiting for Gateway IP address…”rnkubectl wait –for=condition=Programmed=True gateway/inference-gateway –timeout=10mrnexport GATEWAY_IP=$(kubectl get gateway inference-gateway -o jsonpath='{.status.addresses[0].value}’ 2>/dev/null)rnrnif [ -z “$GATEWAY_IP” ]; thenrn echo “Error: Could not retrieve Gateway IP address.”rn kubectl get gateway inference-gateway -o yaml # Print full status for debuggingrnelsern echo “Gateway IP: ${GATEWAY_IP}”rnfi’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef5151100d0>)])]>

2. Send test inference request: Use curl to send a request to the Gateway endpoint.

NOTE: This uses HTTP/80 for simplicity. Production requires HTTPS/443.

code_block: <ListValue: [StructValue([(‘code’, ‘# IMPORTANT: The very first request might take longer as the model fully loads/warms up. Be patient!rncurl -i -X POST http://${GATEWAY_IP}:80/v1/completions \rn-H ‘Content-Type: application/json’ \rn-d ‘{rn “model”: “google/gemma-3-1b-it”,rn “prompt”: “Explain the main benefits of using GKE Inference Gateway for serving LLMs in simple terms.”,rn “max_tokens”: 150,rn “temperature”: 0.7rn}”), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ef5149dd8e0>)])]>

If successful, you’ll receive an HTTP/1.1 200 OK status followed by a JSON response containing the LLM’s output. If you encounter issues, check the Gateway status (kubectl get gateway ... -o yaml) and the logs of your vLLM pods (kubectl logs deployment/gemma-3-1b-deployment).

Take Your LLM Serving to the Next Level

You’ve successfully deployed an LLM behind the GKE Inference Gateway! Now it’s time to explore its powerful features to build truly production-ready systems:

Scale smartly with autoscaling: Don’t guess capacity! Configure a HorizontalPodAutoscaler (HPA) for your gemma-3-1b-deployment. Scale based on the inference_pool_average_kv_cache_utilization metric provided by Inference Gateway. This ensures you scale based on actual AI workload demand, not just CPU/memory.
Gain visibility with monitoring: Keep a close eye on performance. Use the dedicated Inference Gateway dashboards in Cloud Monitoring to track request counts, latency, error rates, and KV Cache metrics at the gateway level. Combine this with backend pod metrics (GPU utilization, vLLM stats) for a complete picture.
Expand your model portfolio: Serve multiple models efficiently. Deploy other models (e.g., Llama 4, Mistral, or your own fine-tuned variants) using separate Deployments and InferencePools. Use advanced HTTPRoute rules (path-based, header-based, or even request-body-based routing via ExtensionRef) to direct traffic to the correct model pool, all behind the same Gateway IP.
Bolster security and reliability: Protect your endpoints. Configure HTTPS on your Gateway listener using Google-managed or custom TLS certificates. Apply Google Cloud Armor policies at the load balancer for robust WAF and DDoS protection. Consider integrating Model Armor for content safety filtering via GCPTrafficExtension.
Deploy larger, more powerful models: Ready for the big leagues? For models like Qwen 3 235B,, select appropriate GPUs (A100, H100), significantly increase resource requests/limits in your Deployment, adjust vLLM parameters (like tensor-parallel-size), and potentially increase probe timeouts. Inference Gateway’s intelligent load balancing and management become even more critical for efficiently handling these demanding workloads.

By combining the capabilities of modern LLMs with the operational power of GKE and Inference Gateway, you can build, manage, and scale sophisticated AI applications effectively on Google Cloud. Dive deeper into the official GKE Inference Gateway documentation for comprehensive configuration details and advanced scenarios.